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This paper introduces a newly developed multi-sensor data fusion for the milling chatter detection with a cheap and easy implementation compared with traditional chatter detection schemes. The proposed multi-sensor data fusion utilizes microphone and accelerometer sensors to measure the occurrence of chatter during the milling process. It has the advantageous over the dynamometer in terms of easy installation and low cost. In this paper, the wavelet packet decomposition is adopted to analyze both measured sound and vibration signals. However, the parameters of the wavelet packet decomposition require fine-tuning to provide good performance. Hence the result of the developed scheme has been improved by optimizing the selection of the wavelet packet decomposition parameters including the mother wavelet and the decomposition level based on the kurtosis and crest factors. Furthermore, the important chatter features are selected using the recursive feature elimination method, and its performance is compared with metaheuristic algorithms. Finally, several machine learning techniques have been adopted to classify the cutting stabilities based on the selected features. The results confirm that the proposed multi-sensor data fusion scheme can provide an effective chatter detection under industrial conditions, and it has higher accuracy than the traditional schemes. • Introducing a cost-effective multi-sensor data fusion for the milling chatter detection. • The WPD is utilized and tuned based on the kurtosis and crest factors. • The irrelevant features are identified and eliminated utilizing the RFE method. • Several machine learning techniques have been adopted to identify chatter vibration. [ABSTRACT FROM AUTHOR]

• The uncertainties due to the cyberattacks are tackled with a low computation burden deep learning algorithm. • A new IoT structure for monitoring the induction motor is developed. • A graphical visualization is performed to identify the faults of the induction motors. • Parameterizing the DNN of the fault recognition model is investigated. • The results confirm the robustness and resiliency of the designed model. Maintaining electrical machines in good working order and increasing their life expectancy is one of the main challenges. Precocious and accurate detection of faults is crucial to this process. Induction motors (IMs) are among these machines widely utilized in various fields including industrial and domestic applications that require effective detection of their status. This paper proposes a novel fault recognition and correction (FRC) scheme based on the internet of things (IoT) and deep learning for IMs. In the developed system, vibration signals during motor operation are used to generate bearing fault features, which are inputted to the designed deep learning model to successfully identify bearing faults. The robustness of the proposed approach is tested against a false data injection (FDI) attack. Further, the proposed deep learning approach has been assessed and compared with other state-of-the-art algorithms available in the literature. Experimental testing has been carried out on a real IM to perform the suitability of the developed fault detection and correction scheme. Compared to other fault recognition techniques, the proposed method proves to be more effective. In essence, the results verified the robustness of the novel proposed strategy against the FDI attack making it possible to recognize faults with confidence and improve decision-making to determine the motor's status. [ABSTRACT FROM AUTHOR]

In the "Industry 4.0" era, autonomous and self-adaptive industrial machining attracts significant attention in professional manufacturing. This trend originates from the rising demands for machining quantity and part quality, as well as the need for optimization of the overall operational cost. Under such circumstances, tool condition monitoring (TCM) systems contribute to the mentioned goals by monitoring the cutting tool operation states, which detect signs of tool failures such as cracks, chippings, fractures, and estimate the remaining tool life for the ultimate goal of optimizing the performance of machining processes. This article presents the latest advancements in each stage of TCM systems, namely fusion sensors methods, modern data acquisition systems (DAQ), virtual machining, and lightweight TCM models, wherein artificial intelligence (AI) and Internet of Things (IoT) technologies demonstrate promising operational efficiency and accuracy while showing their potential in practical applications. Moreover, emphasis is paid to the current blank spots and limitations of implementing machine learning, deep learning, and IoT technologies in tool-embedded TCM, namely big data handling, generalization of Machine Learning models, as well as cloud computing latency, which suggests corresponding solutions to enable the practicality of mass implementation of Industrial Internet of Things (IIoT) for TCM systems, such as cloud migration, custom TCM network, and shared knowledge databases between multiple factories. In addition to a comprehensive review of state-of-the-art innovations for TCM systems, the future prospects of intelligent TCM systems are also presented for smart manufacturing. [ABSTRACT FROM AUTHOR]

• Developing a reliable and secure IoT against transformer cyberattacks. • Introducing a deep 1D-CNN for fault classification against uncertainties. • Deep 1D-CNN is combined with the IoT for online transformer monitoring. • The introduced architecture increases the transformer life time. The distribution of the power transformers at a far distance from the electrical plants represents the main challenge against the diagnosis of the transformer status. This paper introduces a new integration of an Internet of Things (IoT) architecture with deep learning against cyberattacks for online monitoring of the power transformer status. A developed one dimension convolutional neural network (1D-CNN), which is characterized by robustness against uncertainties, is introduced for fault diagnosis of power transformers and cyberattacks. Further, experimental scenarios are performed to confirm the effectiveness of the proposed IoT architecture. While compared to previous approaches in the literature, the accuracy of the new deep 1D-CNN is greater with 94.36 percent in the usual scenario, 92.58 percent when considering cyberattacks, and ±5% uncertainty. The proposed integration between the IoT platform and the 1D-CNN can detect the cyberattacks properly and provide secure online monitoring for the transformer status via the internet network. [ABSTRACT FROM AUTHOR]

Abstract: The patenting activity in the field of controlled nuclear fusion was investigated to assess the role of this emerging technology as a catalyst for inventions in other technological areas. Patent statistical data allows to track not only the evolution of a technology, but also to analyze cross-fertilizing effects of a technology that is not quite ready for implementation. Spillovers from nuclear fusion research for applicable inventions to other technological areas can be identified. [Copyright &y& Elsevier]

• A new feature selection using fuzzy entropy method for chatter diagnosis is proposed. • The measured signals are filtered using EEMD method and analyzed using HHT approach. • The proposed method is compared with different metaheuristics algorithms. • The proposed method has a low computational burden and high classification accuracy. Feature selection represents the main challenge against the classification strategies for several applications of signal processing. Besides, the high computational speed and accuracy represent a critical requirement of chatter diagnosis. In this paper, a new fuzzy entropy measure with a similarity classifier is developed for feature selection vibration diagnosis in CNC machines. The measured signals are filtered using ensemble empirical mode decomposition and analyzed using Hilbert-Huang transform approach. The proposed feature selection technique can decrease noise and this strategy improves the classification accuracy. The proposed fuzzy entropy approach is compared with different feature selection and machine learning methods in the literature. The experimental results and comparative analyses confirm the superiority of the proposed fuzzy entropy method for chatter vibration identification with a low computational burden and a high accuracy of 98% that is significantly greater than other existing intelligent diagnosis techniques. [ABSTRACT FROM AUTHOR]

Induction motors are widely used in various industrial applications due to their simplicity, robustness, and high efficiency. In recent years, deep learning approaches have shown great potential for detecting and diagnosing faults in induction motors. However, it was observed that the convolutional neural network (CNN) might miss the crucial long-range temporal dependencies of the significant features. At the same time, the recursive neural network (RNN) may face issues such as vanishing gradient and less computational efficiency if the data is too long with complex patterns. This paper proposes a novel deep-learning model that combines a one-dimensional CNN and an RNN into two independent pipelines. The spatial, temporal, long-term, and short-term features can be extracted from the raw input signal. A multi-head mechanism, a state-of-the-art deep learning technique in natural language processing (NLP), enables the model to focus more on relevant features and prevent overfitting. Specifically, our method simplifies the conventional fault diagnosis workflow while maintaining competitive accuracy compared to several state-of-the-art methods. By leveraging the ability to detect faults directly from raw, unprocessed electrical signals, our approach offers a practical, cost-effective solution for real-time fault detection in industrial applications. This innovation has the potential to significantly reduce downtime and the associated operational costs, thereby providing immediate practical value in various industrial settings. [ABSTRACT FROM AUTHOR]